In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
Example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Example solution: No
Example explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

Problem: Curriculum Plausibility ::: Conversational Attributes ::: Specificity
A notorious problem for neural dialogue generation model is that the model is prone to generate generic responses. Curriculum Plausibility ::: Conversational Attributes ::: Repetitiveness
Repetitive responses are easy to generate in current auto-regressive response decoding, where response generation loops frequently, whereas diverse and informative responses are much more complicated for neural dialogue generation. Curriculum Plausibility ::: Conversational Attributes ::: Continuity
A coherent response not only responds to the given query, but also triggers the next utterance. Curriculum Plausibility ::: Conversational Attributes ::: Model Confidence
Despite the heuristic dialogue attributes, we further introduce the model confidence as an attribute, which distinguishes the easy-learnt samples from the under-learnt samples in terms of the model learning ability. Curriculum Plausibility ::: Conversational Attributes ::: Query-relatedness
A conversation is considered to be coherent if the response correlates well with the given query. 
 Question: What five dialogue attributes were analyzed?

Solution: Model Confidence Continuity Query-relatedness Repetitiveness Specificity